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FitResSummary.m
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FitResSummary.m
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classdef FitResSummary < handle
% FITRESSUMMARY - given a collection of fitResult objects (one for each
% neuron, each containing the results of multiple regressions), computes
% summary statistics across neurons. This is to allows visualization of
% commonalities in the data across multiple neurons.
%
% % <a href="matlab: methods('FitResSummary')">methods</a>
% see also <a href="matlab:help('FitResult')">FitResult</a>
%
% Reference page in Help browser
% <a href="matlab:doc('FitResSummary')">doc FitResSummary</a>
%
% nSTAT v1 Copyright (C) 2012 Masschusetts Institute of Technology
% Cajigas, I, Malik, WQ, Brown, EN
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License as published
% by the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
% See the GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software Foundation,
% Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
properties
fitResCell %collection of FitResult Objects
fitNames %names of the different fits
numResults %number of fits in each FitResult Object
maxNumIndex %Index into the results that have all of the labels
dev
AIC % AIC matrix (length(neuronNumbers) x numResults)
BIC % BIC matrix (length(neuronNumbers) x numResults)
bAct
seAct
sigIndex
covLabels %labels of relevant covariates in fits.
uniqueCovLabels
indicesToUniqueLabels
flatMask
neuronNumbers % neuronNumber(i) is the number of the neuron corresponding to fitResCell{i}
numNeurons % Total number of neurons being summarized
numCoeffs
numResultsCoeffPresent;
KSStats % KSStats matrix (length(neuronNumbers) x numResults)
KSPvalues
withinConfInt
plotParams
coeffRange
end
methods
function frsObj = FitResSummary(fitResultsCell)
% frsObj = FitResSummary(fitResultsCell)
% Takes a cell array of FitResult objects and returns a
% FitResultSummary object.
if(isa(fitResultsCell,'FitResult'))
frsObj=FitResSummary({fitResultsCell});
elseif(isa(fitResultsCell,'cell') && ~isempty(fitResultsCell))
if(isa(fitResultsCell{1},'FitResult'))
frsObj.fitResCell=cell(length(fitResultsCell),1);
maxNumResults = 0;
maxNumIndex=1;
for i=1:length(fitResultsCell)
if(fitResultsCell{i}.numResults>maxNumResults)
maxNumResults = fitResultsCell{i}.numResults;
maxNumIndex = i;
end
end
frsObj.maxNumIndex = maxNumIndex;
frsObj.dev = nan(length(fitResultsCell),maxNumResults);
frsObj.AIC = nan(length(fitResultsCell),maxNumResults);
frsObj.BIC = nan(length(fitResultsCell),maxNumResults);
frsObj.KSStats = nan(length(fitResultsCell),maxNumResults);
frsObj.KSPvalues = nan(length(fitResultsCell),maxNumResults);
frsObj.withinConfInt = zeros(length(fitResultsCell),maxNumResults);
for i=1:length(fitResultsCell)
frsObj.neuronNumbers(i) = fitResultsCell{i}.neuronNumber;
frsObj.dev(i,1:length(fitResultsCell{i}.dev)) = fitResultsCell{i}.dev(1:end);
frsObj.AIC(i,1:length(fitResultsCell{i}.AIC)) = fitResultsCell{i}.AIC(1:end);
frsObj.BIC(i,1:length(fitResultsCell{i}.BIC)) = fitResultsCell{i}.BIC(1:end);
frsObj.covLabels{i} = fitResultsCell{i}.uniqueCovLabels;
%flatMask(:,:,i) = fitResultsCell{i}.flatMask;
frsObj.fitResCell{i} = fitResultsCell{i};
frsObj.KSStats(i,1:length(fitResultsCell{i}.KSStats.ks_stat)) = fitResultsCell{i}.KSStats.ks_stat(1:end);
frsObj.KSPvalues(i,1:length(fitResultsCell{i}.KSStats.pValue)) = fitResultsCell{i}.KSStats.pValue(1:end);
frsObj.withinConfInt(i,1:length(fitResultsCell{i}.KSStats.withinConfInt)) = fitResultsCell{i}.KSStats.withinConfInt(1:end);
end
frsObj.numResults = maxNumResults;%fitResultsCell{1}.numResults;
frsObj.numNeurons = length(frsObj.neuronNumbers);
frsObj.uniqueCovLabels = getUniqueLabels(frsObj.covLabels);
frsObj.mapCovLabelsToUniqueLabels;
%indActCoeffs = find( sum(squeeze(sum(frsObj.flatMask,2)),2)>0);
bAct=nan(length(frsObj.uniqueCovLabels),frsObj.numResults,frsObj.numNeurons);
seAct=nan(length(frsObj.uniqueCovLabels),frsObj.numResults,frsObj.numNeurons);
sigIndex=zeros(length(frsObj.uniqueCovLabels),frsObj.numResults,frsObj.numNeurons);
for n=1:frsObj.numNeurons
for j=1:frsObj.numResults
index=frsObj.indicesToUniqueLabels{j,n};
if(j<=size(fitResultsCell{n}.flatMask,2))
% origIndex = find(fitResultsCell{n}.flatMask(:,j));
origIndex = fitResultsCell{n}.indicesToUniqueLabels{j};
bAct(index,j,n) = fitResultsCell{n}.getPlotParams.bAct(origIndex,j);
seAct(index,j,n) = fitResultsCell{n}.getPlotParams.seAct(origIndex,j);
sigIndex(index,j,n) = fitResultsCell{n}.getPlotParams.sigIndex(origIndex,j);
end
end
end
frsObj.bAct = bAct;
frsObj.seAct= seAct;
frsObj.sigIndex=sigIndex;
frsObj.numResultsCoeffPresent = (sum(sum(frsObj.flatMask,3),2));
frsObj.numCoeffs = length(frsObj.uniqueCovLabels);
frsObj.coeffRange = [];
frsObj.fitNames= fitResultsCell{maxNumIndex}.configNames;
frsObj.plotParams.bAct = bAct;%(sum(sum(sigIndex,3),2)>0,:,:);
frsObj.plotParams.seAct = seAct;%(sum(sum(sigIndex,3),2)>0,:,:);
frsObj.plotParams.sigIndex = sigIndex;%(sum(sum(sigIndex,3),2)>0,:,:);
frsObj.plotParams.xLabels = frsObj.uniqueCovLabels;%(sum(sum(sigIndex,3),2)>0);
frsObj.plotParams.numResultsCoeffPresent = frsObj.numResultsCoeffPresent;%(sum(sum(sigIndex,3),2)>0);
% frsObj.plotParams.bAct = bAct(sum(sum(sigIndex,3),2)>0,:,:);
% frsObj.plotParams.seAct = seAct(sum(sum(sigIndex,3),2)>0,:,:);
% frsObj.plotParams.sigIndex = sigIndex(sum(sum(sigIndex,3),2)>0,:,:);
% frsObj.plotParams.xLabels = frsObj.uniqueCovLabels(sum(sum(sigIndex,3),2)>0);
% frsObj.plotParams.numResultsCoeffPresent = frsObj.numResultsCoeffPresent(sum(sum(sigIndex,3),2)>0);
%
end
end
end
%% Utility Functions
function mapCovLabelsToUniqueLabels(frsObj)
% mapCovLabelsToUniqueLabels(frsObj)
% from all the covariate labels across all neurons and all
% fits, finds a minimal list of covariate labels to be used in
% summarizing the data.
flatMask = zeros(length(frsObj.uniqueCovLabels),frsObj.numResults,frsObj.numNeurons);
for n=1:frsObj.numNeurons
currFitResult = frsObj.fitResCell{n};
for j=1:currFitResult.numResults
currLabels = currFitResult.covLabels{j};
index=zeros(1,length(currLabels));
for i=1:length(currLabels)
index(i)=strmatch(currLabels{i}, frsObj.uniqueCovLabels, 'exact');
end
frsObj.indicesToUniqueLabels{j,n} = index;
flatMask(index,j,n) = 1;
end
end
frsObj.flatMask = flatMask;
end
function [dAIC, handle] = getDiffAIC(frsObj,diffIndex,makePlot,h)
% [dAIC, handle] = getDiffAIC(frsObj,diffIndex,makePlot,h)
% Takes the AIC matrix and returns a matrix with N-1 columns
% containing the difference between all columns of the original
% matrix minus the column indicated by diffIndex. The zero
% column corresponding to diffIndex is removed from the orginal
% AIC matrix
if(nargin<4)
h=gca;
end
if(nargin<3 || isempty(makePlot))
makePlot=1;
end
if(nargin<2 || isempty(diffIndex))
diffIndex = 1;
end
if(frsObj.numResults>1)
dAIC=computeDiffMat(frsObj.AIC,diffIndex);
else
dAIC=frsObj.AIC;
end
if(makePlot)
handle=frsObj.boxPlot(dAIC,diffIndex,h);
end
end
function [dBIC, handle] = getDiffBIC(frsObj,diffIndex,makePlot,h)
% [dBIC, handle] = getDiffBIC(frsObj,diffIndex,makePlot,h)
% Takes the BIC matrix and returns a matrix with N-1 columns
% containing the difference between all columns of the original
% matrix minus the column indicated by diffIndex. The zero
% column corresponding to diffIndex is removed from the orginal
% BIC matrix
if(nargin<4)
h=gca;
end
if(nargin<3 || isempty(makePlot))
makePlot=1;
end
if(nargin<2 || isempty(diffIndex))
diffIndex = 1;
end
if(frsObj.numResults>1)
dBIC=computeDiffMat(frsObj.BIC,diffIndex);
else
dBIC=frsObj.BIC;
end
if(makePlot==1)
handle=frsObj.boxPlot(dBIC,diffIndex,h);
end
end
function [N,edges,percentSig] = binCoeffs(frsObj,minVal,maxVal,binSize)
% [N,edges,percentSig] = binCoeffs(frsObj,minVal,maxVal,binSize)
% Does a histogram of the regression coefficients across all
% fits. Also returns an indicator of the percentage of times
% that a coefficient was significantly different than zero out
% of all the times that it was used in a regression.
if(nargin<4)
binSize=.1;
end
if(nargin<3)
if(isempty(frsObj.coeffRange))
%v=axis;
maxVal=12;%v(4);
frsObj.coeffRange.maxVal = maxVal;
else
%if(exists(frsObj.coeffRange.maxVal))
maxVal=frsObj.coeffRange.maxVal;
%else
%maxVal = 12;
%end
end
end
if(nargin<2)
if(isempty(frsObj.coeffRange))
% v=axis;
minVal=-12;%v(3);
frsObj.coeffRange.minVal = minVal;
else
%if(exists(frsObj.coeffRange.maxVal))
minVal=frsObj.coeffRange.minVal;
%else
% minVal=-12;
%end
end
end
edges=(minVal:binSize:maxVal)';
sigVals = frsObj.plotParams.bAct;%frsObj.plotParams.sigIndex;
numPlotCoeffs = length(frsObj.plotParams.xLabels);
numSig = zeros(1,numPlotCoeffs);
percentSig=zeros(1,numPlotCoeffs);
sigValArray=[];
sigGroup=[];
for i=1:numPlotCoeffs %num coefficients
tempsigVals = squeeze(sigVals(i,:,:));
tempsigVals = tempsigVals(squeeze(frsObj.plotParams.sigIndex(i,:,:))==1);
%sigValArray = [sigValArray;tempsigVals];
sigGroup = [sigGroup; repmat(i,[length(tempsigVals),1])];
Ntemp=histc(tempsigVals,edges);
numSig(i) = sum(Ntemp);
[nr,nc] = size(squeeze(sigVals(i,:,:))); %for this coefficient across all fits
%percentSig(i) = numSig(i)./(nr*nc)*frsObj.numResults./frsObj.plotParams.numResultsCoeffPresent(i);
percentSig(i) = numSig(i)./frsObj.plotParams.numResultsCoeffPresent(i);
N(:,i)=Ntemp./numSig(i); %normalize to 1 (pdf)
end
end
function setCoeffRange(frsObj,minVal,maxVal)
% setCoeffRange(frsObj,minVal,maxVal)
% Sets the minimum and maximum value for the coeffRange.
frsObj.coeffRange.minVal=max(-100,minVal);
frsObj.coeffRange.maxVal=min(100,maxVal);
end
function sigValMat = getSigCoeffs(frsObj,fitNum)
% sigValMat = getSigCoeffs(frsObj,fitNum)
% if a fitNum is specified, a 2-d matrix (number of rows =
% number of GLM coefficients for that fit, number of columns =
% numer of neurons)
% Otherwise returns a 3-d matrix indicating the significant
% coefficients across all regression coefficient, fits, and
% neurons.
% nUnique params x num regressions x num neurons in size.
sigValMat = frsObj.plotParams.bAct.*frsObj.plotParams.sigIndex;
if(nargin==2 && fitNum>0 && fitNum<frsObj.numResults)
sigValMat = squeeze(sigValMat(:,fitNum,:));
end
end
%% Plotting Functions
function handle = plotIC(frsObj,h)
% handle = plotIC(frsObj,h)
% Plots the difference in AIC and BIC from baseline (first regression).
if(nargin<2)
h(1) = subplot(2,1,1);
h(2) = subplot(2,1,2);
end
makePlot=1;
% [~, h1] = frsObj.getDiffAIC(1,makePlot,h(1)); ylabel('\Delta AIC');
% [~, h2] = frsObj.getDiffBIC(1,makePlot,h(2)); ylabel('\Delta BIC');
subplot(2,1,1); h1=frsObj.getDiffAIC(1); ylabel('\Delta AIC');
subplot(2,1,2); h2=frsObj.getDiffBIC(1); ylabel('\Delta BIC');
handle = [h1,h2];
end
function handle = plotAllCoeffs(frsObj,h,fitNum,plotProps,plotSignificance,subIndex)
% handle = plotAllCoeffs(frsObj,h)
% plots the GLM coefficients for each unique covariate across
% the multiple types of regressions and across neurons.
if(nargin<5 || isempty(plotSignificance))
plotSignificance = 1;
end
if(nargin<4 || isempty(plotProps))
plotProps = [];
end
if(nargin<3)
fitNum = 1:frsObj.numResults;
end
if(nargin<2)
h = gca;
end
if(nargin<6 || isempty(subIndex))
subIndex = 1:size(frsObj.plotParams.bAct,1);
end
neuronIndex = 1:frsObj.numNeurons;
bAct = frsObj.plotParams.bAct(subIndex,fitNum,neuronIndex);
seAct= frsObj.plotParams.seAct(subIndex,fitNum,neuronIndex);
Xaxis=repmat(1:length(bAct(:,1,1)),[length(bAct(1,:,1)) 1])';
for i=1:frsObj.numNeurons
set(gcf,'CurrentAxes',h);
handle=errorbar(Xaxis,squeeze(bAct(:,:,i)),squeeze(seAct(:,:,i)),'.');%strcat('.',FitResult.colors{mod(i-1,length(FitResult.colors))+1}));
hold on;
end
%
% for j=1:length(frsObj.uniqueCovLabels)
% for i=1:frsObj.numResults
% % set(gcf,'CurrentAxes',h);
% % handle=errorbar(Xaxis,squeeze(bAct(:,:,i)),squeeze(seAct(:,:,i)),'.');%strcat('.',FitResult.colors{mod(i-1,length(FitResult.colors))+1}));
% % boxplot(frsObj.KSStats,frsObj.fitNames);
% boxplot(squeeze(bAct(i,j,:)))
% hold all;
% end
% end
%
hy=ylabel('Fit Coefficients','Interpreter','none');
xtickLabels = frsObj.plotParams.xLabels(subIndex);
xticks = 1:(length(xtickLabels));
set(gca,'xTick',xticks,'xTickLabel',xtickLabels,'FontSize',8);
%hT=rotateticklabel(gca,-90);
h_legend=legend(handle,frsObj.fitResCell{frsObj.maxNumIndex}.lambda.dataLabels(fitNum),'Location','SouthEast');
set(h_legend,'FontSize',10)
hx=get(gca,'XLabel');
set([hx hy],'FontName', 'Arial','FontSize',12,'FontWeight','bold');
v=axis;
frsObj.setCoeffRange(v(3),v(4));
grid on;
axis tight;
if(frsObj.numCoeffs>1)
xticklabel_rotate([],90,[],'Fontsize',10);%rotateticklabel(gca,-90);
end
end
function handle = plot3dCoeffSummary(frsObj,h)
% handle = plot3dCoeffSummary(frsObj,h)
% x-axis (covariate labels)
% y-axis (coefficient values)
% z-axis (histogram of coefficient values)
if(nargin<2)
h=gca;
end
frsObj.plotAllCoeffs(h);
[N,edges] = frsObj.binCoeffs;
numPlotCoeffs = length(frsObj.plotParams.xLabels);
handle=ribbon(repmat(edges,[1 numPlotCoeffs]),N);
set(handle,'edgecolor','none');
alpha(.6);
legend off;
view(gca,[71.5 28]);
set(gca,'Box','off', 'Projection','perspective','Color',[0.831372549019608 0.815686274509804 0.784313725490196]);
grid on; axis tight;
end
function handle = plot2dCoeffSummary(frsObj,h)
% handle = plot2dCoeffSummary(frsObj,h)
% histogram of regression coefficients for each unique
% covariate.
if(nargin<2)
h=gca;
end
[N,edges,percentSig] = frsObj.binCoeffs;
offset=0;
numPlotCoeffs = length(frsObj.plotParams.xLabels);
for i=1:numPlotCoeffs
offset=offset+1;
handle(i)=plot(h,edges,N(:,i)+offset); hold on;
% plot(edges,N(:,i)+offset,'.');
set(gca,'xtick',[],'ytick',[]);
end
% Reset the bottom subplot to have xticks
set(gca,'xtickMode', 'auto');
set(gca,'ytick',1:length(frsObj.plotParams.xLabels),'ytickLabel',frsObj.plotParams.xLabels,'FontSize',6);
offset=0;
for i=1:numPlotCoeffs
offset=offset+1;
text(frsObj.coeffRange.maxVal,offset,strcat(num2str(percentSig(i)*100,'%2.f'),'%_{sig}'),'FontSize',6); hold on;
end
end
function handle = plotKSSummary(frsObj,neurons)
% handle = plotKSSummary(frsObj,neurons)
% For all of the distinct neurons in the the FitResSummary,
% plots the corresponding KS plot with all the differentt fits
% available for each neuron. Each neuron may have different numbers
% of model fits: eg. Neuron j may have 3 fits while
% neuron i may have 6.
if(nargin<2||isempty(neurons))
neurons = 1:frsObj.numNeurons;
end
if(max(neurons)>frsObj.numNeurons)
error('Indices must be <= numNeurons');
end
handle = figure;
numToPlot=length(neurons);
cnt=0;
for i=neurons
cnt=cnt+1;
if(numToPlot==1)
%dont subplot
elseif(numToPlot<=2)
subplot(1,2,cnt);
elseif(numToPlot<=4)
subplot(2,2,cnt)
elseif(numToPlot<=8)
subplot(2,4,cnt)
elseif(numToPlot<=12)
subplot(3,4,cnt)
elseif(numToPlot<=16)
subplot(4,4,cnt)
elseif(numToPlot<=20)
subplot(5,4,cnt)
elseif(numToPlot<=24)
subplot(6,4,cnt)
elseif(numToPlot<=40)
subplot(10,4,cnt)
else
subplot(10,10,cnt)
end
frsObj.fitResCell{i}.KSPlot;
if(i~=neurons(end))
legend off; ylabel(''); xlabel(''); title('');
else
h= legend; set(h,'Location','Best'); ylabel(''); xlabel(''); title('');
end
text(.4,.9,['N' num2str(i)]);
set(gca,'xtick',[0 1],'ytick',[0 1])
end
end
function handle = plotAIC(frsObj)
% handle = plotAIC(frsObj)
% Plot mean +/- 1 standard error from the mean for the AIC for
% each fit.
AICdata=frsObj.AIC;
mData=nanmean(AICdata,1);
numNeurons = size(AICdata,1);
sData=nanstd(AICdata,0,1)./sqrt(numNeurons);
ciUp = mData+sData;
ciDown = mData-sData;
x=1:frsObj.numResults;
plot(x,mData,'r','Linewidth',3); hold all;
faceColor='r';
p=patch([x, fliplr(x)],[ciUp fliplr(ciDown)],faceColor);
set(p,'facecolor',faceColor,'edgecolor','none');
alpha(.5);
% set(gca,'xticklabelmode','auto','xtickmode','auto');
set(gca,'xtick',x,'xticklabel',frsObj.fitNames);
if(length(x)>1)
xticklabel_rotate([],90,[],'Fontsize',8);%rotateticklabel(gca,-90);
end
end
function handle = plotBIC(frsObj)
% handle = plotBIC(frsObj)
% Plot mean +/- 1 standard error from the mean for the BIC for
% each fit.
BICdata=frsObj.BIC;
mData=nanmean(BICdata,1);
numNeurons = size(BICdata,1);
sData=nanstd(BICdata,0,1)./sqrt(numNeurons);
ciUp = mData+sData;
ciDown = mData-sData;
x=1:frsObj.numResults;
plot(x,mData,'r','Linewidth',3); hold all;
faceColor='r';
p=patch([x, fliplr(x)],[ciUp fliplr(ciDown)],faceColor);
set(p,'facecolor',faceColor,'edgecolor','none');
alpha(.5);
% set(gca,'xticklabelmode','auto','xtickmode','auto');
set(gca,'xtick',x,'xticklabel',frsObj.fitNames);
if(length(x)>1)
xticklabel_rotate([],90,[],'Fontsize',8);%rotateticklabel(gca,-90);
end
end
function handle = plotResidualSummary(frsObj)
handle = figure;
for i=1:frsObj.numNeurons
if(frsObj.numNeurons<=4)
subplot(2,2,i)
elseif(frsObj.numNeurons<=8)
subplot(2,4,i)
elseif(frsObj.numNeurons<=12)
subplot(3,4,i)
elseif(frsObj.numNeurons<=16)
subplot(4,4,i)
elseif(frsObj.numNeurons<=20)
subplot(5,4,i)
elseif(frsObj.numNeurons<=24)
subplot(6,4,i)
elseif(frsObj.numNeurons<=40)
subplot(10,4,i)
else
subplot(10,10,i)
end
frsObj.fitResCell{i}.plotResidual;
if(i~=frsObj.numNeurons)
legend off; ylabel(''); xlabel(''); title('');
else
h= legend; set(h,'Location','BestOutside'); ylabel(''); xlabel(''); title('');
end
end
end
function handle = plotSummary(frsObj)
% handle = plotSummary(frsObj)
%
scrsz = get(0,'ScreenSize');
handle=figure('OuterPosition',[scrsz(3)*.1 scrsz(4)*.1 scrsz(3)*.9 scrsz(4)*.9]);
h1=subplot(2,4,[1 2 5 6]);frsObj.plotAllCoeffs(h1); grid off;
title({'GLM Coefficients Across Neurons';'with 95% CIs (* p<0.05)'},'FontWeight','bold','FontSize',11,'FontName','Arial');
%subplot(2,4,[2 3]);frsObj.plot3dCoeffSummary; %rotateticklabel(get(gca,'ytickLabels'),0);
subplot(2,4,[3 4]); boxplot(frsObj.KSStats,frsObj.fitNames,'labelorientation','inline');
ylabel('KS Statistics');
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',11,'FontWeight','bold');
title('KS Statistics Across Neurons','FontWeight','bold','FontSize',11,'FontName','Arial');
% subplot(2,4,[6 7]);frsObj.plot2dCoeffSummary; %rotateticklabel(get(gca,'ytickLabels'),0);
subplot(2,4,7); frsObj.getDiffAIC(1);
ylabel('\Delta AIC'); %xticklabel_rotate([],45,[],'Fontsize',6);
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',11,'FontWeight','bold');
title('Change in AIC Across Neurons','FontWeight','bold','FontSize',11,'FontName','Arial');
subplot(2,4,8); frsObj.getDiffBIC(1);
ylabel('\Delta BIC'); %xticklabel_rotate([],45,[],'Fontsize',6);
hx=get(gca,'XLabel'); hy=get(gca,'YLabel');
set([hx hy],'FontName', 'Arial','FontSize',11,'FontWeight','bold');
title('Change in BIC Across Neurons','FontWeight','bold','FontSize',11,'FontName','Arial');
end
function handle = boxPlot(frsObj,X,diffIndex,h,dataLabels,varargin)
if(nargin<3)
h=gca;
end
if(nargin<5 || isempty(dataLabels))
[~,columns] = size(X);
tempIndex = 1:frsObj.numResults;
actIndex = find(tempIndex~=diffIndex);
if(~isempty(actIndex))
for i=1:columns
if(length(actIndex)==columns)
dataLabels{i} = [frsObj.fitNames{actIndex(i)} ' - ' frsObj.fitNames{diffIndex}];
end
end
else
dataLabels{1}=frsObj.fitNames{diffIndex}; %only put the name of the fit since no other fits
end
if(columns>1)
handle = boxplot(X,strvcat(dataLabels));
else
handle = boxplot(X,dataLabels); % when only one column
end
% set(gca,'xticklabelmode','auto','xtickmode','auto');
% set(gca,'xtick',1:length(dataLabels),'xticklabel',dataLabels);
% hT=rotateticklabel(gca,90);
% FitResSummary.xticklabel_rotate([],45,[],'interpreter','none');
elseif(nargin>5)
handle = boxplot(h,X,strvcat(dataLabels),varargin{:});
elseif(nargin==5)
handle = boxplot(h,X,strvcat(dataLabels));
end
h = get(get(gca,'child'),'child');
group_name_handle = findobj(h,'type','text');
group_name_handle = flipud(group_name_handle); %place in correct order - find obj returns backwards
v=axis;
vdiffy = v(4)-v(3);
vdiffx = v(2)-v(1);
for j=1:length(group_name_handle)
text(0,0,get(group_name_handle(j),'string'),'color','k','position',[j-.0*vdiffx v(3)-.02*vdiffy 0],'rotation',-90,'Fontsize',8);
end
delete(group_name_handle);
end
function structure = toStructure(frsObj)
fNames = fieldnames(frsObj);
for i=1:length(fNames)
currObj = frsObj.(fNames{i});
if(isa(currObj,'double')||isa(currObj,'cell'))
if(strcmp(fNames{i},'fitResCell'))
structure.(fNames{i}) = FitResult.CellArrayToStructure(frsObj.(fNames{i}));
else
structure.(fNames{i}) = frsObj.(fNames{i});
end
end
end
end
function [coeffIndex, epochId,numEpochs] = getCoeffIndex(frsObj,fitNum,sortByEpoch)
if(nargin<3 || isempty(sortByEpoch))
sortByEpoch=0;
end
if(nargin<2 || isempty(fitNum))
fitNum = 1:frsObj.numResults;
end
if(isempty(frsObj.plotParams))
frsObj.computePlotParams;
end
[histIndex, epochId] = frsObj.getHistIndex(fitNum,sortByEpoch);
allIndex = 1:length(frsObj.uniqueCovLabels);
nonHistIndex = setdiff(allIndex,histIndex);
nonNANIndex = find(sum(~isnan(squeeze(frsObj.plotParams.bAct(:,fitNum,:))),2)>=1);
actCoeffIndex = nonHistIndex(ismember(nonHistIndex, nonNANIndex));
allCoeffTerms = frsObj.uniqueCovLabels(actCoeffIndex);
epochStartInd=regexp(allCoeffTerms,'_\{\d*\}','start');
epochEndInd=regexp(allCoeffTerms,'_\{\d*\}','end');
allCoeffIndex = [];
epochsExist =0;
nonEpochIndex=[];
for i=1:length(allCoeffTerms)
if(~isempty(allCoeffTerms{i}))
allCoeffIndex = [allCoeffIndex i];
if(~isempty(epochStartInd{i}))
epochsExist=1;
actStart = epochStartInd{i}+2;
actEnd = epochEndInd{i}-1;
numEpoch(i) = str2num(allCoeffTerms{i}(actStart:actEnd));
else
nonEpochIndex = [nonEpochIndex i];
numEpoch(i) = 0; % make terms that only appear once part of epoch 0.
end
end
end
if(epochsExist && ~sortByEpoch)
totalEpochs = unique(numEpoch);
coeffIndex = [];
if(nargout>1)
epochId=[];
end
for i=1:length(totalEpochs)
coeffIndex = [coeffIndex, find(numEpoch==totalEpochs(i))];
if(nargout>1)
epochId = [epochId, totalEpochs(i)*ones(size(find(numEpoch==totalEpochs(i))))];
end
end
coeffIndex = actCoeffIndex(coeffIndex);
elseif(epochsExist && sortByEpoch)
coeffIndex = actCoeffIndex(allCoeffIndex);
if(nargout>1)
epochId = numEpoch;
end
else
coeffIndex = actCoeffIndex(allCoeffIndex);
if(nargout>1)
epochId = zeros(size(allCoeffIndex)); %no epochs exist so just create same index for all;
end
end
% nonNANIndex = find(sum(~isnan(frsObj.plotParams.bAct(:,fitNum)),2)>=1);
% coeffIndex = coeffIndex(ismember(coeffIndex, nonNANIndex));
%
if(nargout>2)
numEpochs = length(unique(epochId));
end
end
function h=plotCoeffsWithoutHistory(frsObj,fitNum,sortByEpoch,plotSignificance)
if(nargin<4 || isempty(plotSignificance))
plotSignificance=1;
end
if(nargin<3 || isempty(sortByEpoch))
sortByEpoch = 0;
end
if(nargin<2 || isempty(fitNum))
fitNum = 1:frsObj.numResults;
end
if(isempty(frsObj.plotParams))
frsObj.computePlotParams;
end
coeffIndex = frsObj.getCoeffIndex(fitNum,sortByEpoch);
h=frsObj.plotAllCoeffs([],fitNum,[],plotSignificance,coeffIndex);
end
function [histIndex, epochId,numEpochs] = getHistIndex(frsObj,fitNum,sortByEpoch)
%if sortByEpoch==1 then we group all regression terms with the
%same name one next to each other by epoch (time interval).
%Otherwise, we show all epoch one terms, followed by all epoch
%2 terms, etc.
if(nargin<3 || isempty(sortByEpoch))
sortByEpoch = 0;
end
if(nargin<2 || isempty(fitNum))
fitNum = 1:frsObj.numResults;
end
if(isempty(frsObj.plotParams))
frsObj.computePlotParams;
end
allHistTerms = regexp(frsObj.uniqueCovLabels,'^[\w*');
epochStartInd=regexp(frsObj.uniqueCovLabels,'\]_\{\d*\}','start');
epochEndInd=regexp(frsObj.uniqueCovLabels,'\]_\{\d*\}','end');
allHistIndex = [];
epochsExist =0;
for i=1:length(allHistTerms)
if(~isempty(allHistTerms{i}))
allHistIndex = [allHistIndex i];
if(~isempty(epochStartInd{i}))
epochsExist=1;
actStart = epochStartInd{i}+3;
actEnd = epochEndInd{i}-1;
numEpoch(i) = str2num(frsObj.uniqueCovLabels{i}(actStart:actEnd));
end
end
end
if(epochsExist && ~sortByEpoch)
totalEpochs = unique(numEpoch);
histIndex = [];
if(nargout>1)
epochId=[];
end
for i=1:length(totalEpochs)
histIndex = [histIndex, find(numEpoch==totalEpochs(i))];
if(nargout>1)
epochId = [epochId, totalEpochs(i)*ones(size(find(numEpoch==totalEpochs(i))))];
end
end
elseif(epochsExist && sortByEpoch)
histIndex = allHistIndex;
if(nargout>1)
epochId = numEpoch;
end
else
histIndex = allHistIndex;
if(nargout>1)
epochId = zeros(size(allHistIndex)); %no epochs exist so just create same index for all;
end
end
nonNANIndex = find(sum(~isnan(frsObj.plotParams.bAct(:,fitNum)),2)>=1);
histIndex = histIndex(ismember(histIndex, nonNANIndex));
if(nargout>2)
numEpochs = length(unique(epochId));
end
end
function [coeffMat, labels, seMat] = getCoeffs(frsObj, fitNum)
if(nargin<2 || isempty(fitNum))
fitNum =1:frsObj.numResults;
end
sortByEpoch = 0; % Make sure we have different time series if the history is divided into epochs;
[coeffIndex, epochId, numEpochs] = frsObj.getCoeffIndex(fitNum,sortByEpoch);
epochNums = unique(epochId);
coeffStrings = frsObj.uniqueCovLabels(coeffIndex);
baseStringEndIndex =regexp(coeffStrings,'_\{\d*\}','start');
for i=1:length(baseStringEndIndex)
if(~isempty(baseStringEndIndex{i}))
baseStrings{i} = coeffStrings{i}(1:baseStringEndIndex{i}-1);
else
baseStrings{i} = coeffStrings{i};
end
end
uniqueCoeffs = unique(baseStrings);
for i=1:length(uniqueCoeffs)
coeffStrIndex{i} = coeffIndex(strcmp(baseStrings,uniqueCoeffs{i}));
if(min(epochId)==0)
epochIndices{i} = epochId(strcmp(baseStrings,uniqueCoeffs{i}))+1;
else
epochIndices{i} = epochId(strcmp(baseStrings,uniqueCoeffs{i}));
end
end
%
% for i=1:numEpochs
% epochIndices{i} = find(epochId==epochNums(i));
% epochLength(i) = length(epochIndices{i});
% end
coeffIndMat= nan(length(uniqueCoeffs),numEpochs);
labels = cell(size(coeffIndMat));
for i=1:length(uniqueCoeffs)
coeffIndMat(i,epochIndices{i}) = coeffStrIndex{i};
labels(i,epochIndices{i}) = frsObj.uniqueCovLabels(coeffStrIndex{i});
end
% for i=1:numEpochs
% coeffIndMat(1:epochLength(i),i) = coeffIndex(epochIndices{i});
% labels(1:epochLength(i),i) = frsObj.uniqueCovLabels(coeffIndMat(1:epochLength(i),i));
% end
if(length(fitNum)>1)
coeffMat=cell(1,length(fitNum));
seMat=cell(1,length(fitNum));
for i=1:length(fitNum)
coeffMat{i} = nan(size(coeffIndMat,1),size(coeffIndMat,2), frsObj.numNeurons);
seMat{i} = nan(size(coeffIndMat,1),size(coeffIndMat,2), frsObj.numNeurons);
for j=1:length(uniqueCoeffs)
bTemp=squeeze(frsObj.plotParams.bAct(coeffStrIndex{j},i,:));
seTemp=squeeze(frsObj.plotParams.seAct(coeffStrIndex{j},i,:));
for k=1:frsObj.numNeurons
if(size(epochIndices,2)==1)
if(size(bTemp,2)==1)
coeffMat(j,epochIndices{1},k) = bTemp(k);
seMat(j,epochIndices{1},k) = seTemp(k);
else
coeffMat(j,epochIndices{1},k) = bTemp(:,k);
seMat(j,epochIndices{1},k) = seTemp(:,k);
end
else
if(size(bTemp,2)==1)
coeffMat{i}(j,epochIndices{j},k) = bTemp(k);
seMat{i}(j,epochIndices{j},k) = seTemp(k);
else
coeffMat{i}(j,epochIndices{j},k) = bTemp(:,k);
seMat{i}(j,epochIndices{j},k) = seTemp(:,k);
end
end
end
end
end
else
coeffMat = nan(size(coeffIndMat,1),size(coeffIndMat,2),frsObj.numNeurons);
seMat = nan(size(coeffIndMat,1),size(coeffIndMat,2),frsObj.numNeurons);
for j=1:length(uniqueCoeffs)
bTemp=squeeze(frsObj.plotParams.bAct(coeffStrIndex{j},fitNum,:));
seTemp=squeeze(frsObj.plotParams.seAct(coeffStrIndex{j},fitNum,:));
for k=1:frsObj.numNeurons
if(size(epochIndices,2)==1)
if(size(bTemp,2)==1)
if(numel(bTemp)==numel(epochIndices{1}) && frsObj.numNeurons==1)
coeffMat(j,epochIndices{1},k) = bTemp(:);
seMat(j,epochIndices{1},k) = seTemp(:);
else
coeffMat(j,epochIndices{1},k) = bTemp(k);
seMat(j,epochIndices{1},k) = seTemp(k);
end
else
coeffMat(j,epochIndices{1},k) = bTemp(:,k);
seMat(j,epochIndices{1},k) = seTemp(:,k);
end
else
if(size(bTemp,2)==1)
if(numel(bTemp)==numel(epochIndices{j}) && frsObj.numNeurons==1)
coeffMat(j,epochIndices{j},k) = bTemp(:);
seMat(j,epochIndices{j},k) = seTemp(:);
else
coeffMat(j,epochIndices{j},k) = bTemp(k);
seMat(j,epochIndices{j},k) = seTemp(k);
end
else
coeffMat(j,epochIndices{j},k) = bTemp(:,k);
seMat(j,epochIndices{j},k) = seTemp(:,k);
end
end
end
end
end
if(frsObj.numNeurons==1)
coeffMat=coeffMat';
seMat=seMat';
end
end
function [histMat, labels] = getHistCoeffs(frsObj,fitNum)
if(nargin<2 || isempty(fitNum))
fitNum =1:frsObj.numResults;
end
sortByEpoch = 0; % Make sure we have different time series if the history is divided into epochs;
[histIndex, epochId, numEpochs] = frsObj.getHistIndex(fitNum,sortByEpoch);
epochNums = unique(epochId);
histcoeffStrings = frsObj.uniqueCovLabels(histIndex);
baseStringEndIndex =regexp(histcoeffStrings,'_\{\d\}','start');
for i=1:length(baseStringEndIndex)
if(~isempty(baseStringEndIndex{i}))
baseStrings{i} = histcoeffStrings{i}(1:baseStringEndIndex{i}-1);
else
baseStrings{i} = histcoeffStrings{i};
end
end
uniqueCoeffs = unique(baseStrings);
for i=1:length(uniqueCoeffs)
histcoeffStrIndex{i} = histIndex(strcmp(baseStrings,uniqueCoeffs{i}));
if(min(epochId)==0)
epochIndices{i} = epochId(strcmp(baseStrings,uniqueCoeffs{i}))+1;
else
epochIndices{i} = epochId(strcmp(baseStrings,uniqueCoeffs{i}));
end
end
%
% for i=1:numEpochs
% epochIndices{i} = find(epochId==epochNums(i));
% epochLength(i) = length(epochIndices{i});
% end
histcoeffIndMat= nan(length(uniqueCoeffs),numEpochs);
labels = cell(size(histcoeffIndMat));
for i=1:length(uniqueCoeffs)
histcoeffIndMat(i,epochIndices{i}) = histcoeffStrIndex{i};
labels(i,epochIndices{i}) = frsObj.uniqueCovLabels(histcoeffStrIndex{i});
end
% for i=1:numEpochs
% coeffIndMat(1:epochLength(i),i) = coeffIndex(epochIndices{i});
% labels(1:epochLength(i),i) = frsObj.uniqueCovLabels(coeffIndMat(1:epochLength(i),i));
% end
if(length(fitNum)>1)
% histMat = nan(size(histcoeffIndMat,1),size(histcoeffIndMat,2), length(fitNum));
histMat = cell(1, length(fitNum));
for i=fitNum
histMat{i} = nan(size(histcoeffIndMat,1),size(histcoeffIndMat,2), frsObj.numNeurons);
for j=1:length(uniqueCoeffs)
bTemp=squeeze(frsObj.plotParams.bAct(histcoeffStrIndex{j},i,:));
for k=1:frsObj.numNeurons
histMat{i}(j,epochIndices{j},k) = bTemp(k,:);